I've tried tensorflow on both cuda 7.5 and 8.0, w/o cudnn (my GPU is old, cudnn doesn't support it). When I execute device_lib.list_local_devices(), there is
I'm using the following generator: datagen = ImageDataGenerator( fill_mode='nearest', cval=0, rescale=1. / 255, rotation_range=90, width_sh
Below is the order of how I am going to present my problem: First I will show you the script .py that I am using to run the web app in a local host(flask app).
I am trying to build a CNN model to recognise human sketch using the TU-Berlin dataset. I downloaded the png zip file, imported the data to Google Colab and the
How can you write a python script to read Tensorboard log files, extracting the loss and accuracy and other numerical data, without launching the GUI tensorboar
I have a flask application that I would like to run it on an EC2 instance and TensorFlow is needed cause it is image classification. However, after the necessar
I am trying to learn and understand how to implement multiclass classification using ANN. In my case, I have 16 classes(0-15), and my label dataset contains one
I'm trying to train research model ssd_mobilenet_v1_fpn_640x640_coco17_tpu-8 using the MultiWorkerMirroredStrategy (by setting --num_workers=2 in the invocation
I am trying to import import tensorflow.python.keras.applications but it gives the bellow error: ModuleNotFoundError: No module named 'tensorflow.python.keras.
I have a conda env that I build from a requirements.yml file that I obtained from a classmate so we could work on a project together. I tried installing matplot
I am using the Physics Informed Neural Networks (PINNs) methodology to solve non-linear PDEs in high dimension. Specifically, I am using this class https://git
I am training a U-Net in keras by minimizing the dice_loss function that is popularly used for this problem: adapted from here and here def dsc(y_true, y_pred)
I am new to Tensorflow and deep leaning. I am trying to see how the loss decreases over 10 epochs in my RNN model that I created to read a dataset from kaggle w
tf.unique currently only works on 1D tensors. How can I find unique values in a 2D tensor. ip=tf.constant([[1,2,1],[3,4,1],[5,6,1],[1,2,1]]) #op should be = [
tf.unique currently only works on 1D tensors. How can I find unique values in a 2D tensor. ip=tf.constant([[1,2,1],[3,4,1],[5,6,1],[1,2,1]]) #op should be = [
Hey everyone this is my first question post. If I do something wrong or u need more information please just tell me I will try to give my best. I tried to creat
Do you know any elegant way to do inference on 2 python processes with 1 GPU tensorflow? Suppose I have 2 processes, first one is classifying cats/dogs, 2nd on
What does it mean to "unroll a RNN dynamically". I've seen this specifically mentioned in the Tensorflow source code, but I'm looking for a conceptual explanati
# lstm model import tensorflow as tf from numpy import mean from numpy import std from numpy import dstack from pandas import read_csv from tensorflow.keras.mod
How can I have python3.6 in tensorflow docker images. All the images I tried (latest, nighty) are using python3.5 and I don't want to modify all my scripts.